Relevance of phase information for object classification in automotive ultrasonic sensing using convolutional neural networks.

Automotive ultrasonic sensors come into play for close-range surround sensing in parking and maneuvering situations. In addition to ultrasonic ranging, classifying obstacles based on ultrasonic echoes to improve environmental perception for advanced driver-assistance systems is an ongoing research topic. Related studies consider only magnitude-based features for classification. However, the phase of an echo signal contains relevant information for target discrimination. This study discusses and evaluates the relevance of the target phase in echo signals for object classification in automotive ultrasonic sensing based on lab and field measurements. Several phase-aware features in the time domain and time-frequency features based on the continuous wavelet transform are proposed and processed using a convolutional neural network. Indeed, phase features are found to contain relevant information, producing only 4% less classification accuracy than magnitude features when the phase is appropriately processed. The investigation reveals high redundancy when magnitude and phase features are jointly fed into the neural network, especially when dealing with time-frequency features. However, incorporating the target phase information facilitates the identification quality in high clutter environments, increasing the model's robustness against signals with low signal-to-noise ratios. Ultimately, the presented work takes one further step toward enhanced object discrimination in advanced driver-assistance systems.

Realistic prediction and engineering of high-Q modes to implement stable Fano resonances in acoustic devices.

Quasi-bound states in the continuum (QBICs) coupling into the propagating spectrum manifest themselves as high-quality factor (Q) modes susceptible to perturbations. This poses a challenge in predicting stable Fano resonances for realistic applications. Besides, where and when the maximum field enhancement occurs in real acoustic devices remains elusive. In this work, we theoretically predict and experimentally demonstrate the existence of a Friedrich-Wintgen BIC in an open acoustic cavity. We provide direct evidence for a QBIC by mapping the pressure field inside the cavity using a Laser Doppler Vibrometer (LDV), which provides the missing field enhancement data. Furthermore, we design a symmetry-reduced BIC and achieve field enhancement by a factor of about three compared to the original cavity. LDV measurements are a promising technique for obtaining high-Q modes' missing field enhancement data. The presented results facilitate the future applications of BICs in acoustics as high-intensity sound sources, filters, and sensors.

Convolutional neural network with data augmentation for object classification in automotive ultrasonic sensing.

Today's low-cost automotive ultrasonic sensors perform distance measurements of obstacles within the close range of vehicles. For future parking assist systems and autonomous driving applications, the performance of the sensors should be further increased. This paper examines the processing of sensor data for the classification of different object classes and traversability of obstacles using a single ultrasonic sensor. The acquisition of raw time signals, transformation into time-frequency images, and classification using machine learning methods are described. Stationary and dynamic measurements at a velocity of 0.5 m/s of various objects have been carried out in a semi-anechoic chamber and on an asphalt parking space. We propose a scalogram-based signal processing chain and a convolutional neural network, which outperforms a LeNet-5-like baseline. Additionally, several methods for offline and online data augmentation are presented and evaluated. It is shown that carefully selected augmentation methods are useful to train more robust models. Accuracies of 90.1% are achieved for the classification of seven object classes in the laboratory and 66.4% in the outdoor environment. Traversability is correctly classified at an accuracy of 96.4% and 91.5%, respectively.

Living in a box: Understanding acoustic parameters in the NICU environment.

Background: In the last years, a significant body of scientific literature was dedicated to the noisy environment preterm-born infants experience during their admission to Neonatal Intensive Care Units (NICUs). Nonetheless, specific data on sound characteristics within and outside the incubator are missing. Therefore, this study aimed to shed light on noise level and sound characteristics within the incubator, considering the following domain: environmental noise, incubator handling, and respiratory support. Methods: The study was performed at the Pediatric Simulation Center at the Medical University of Vienna. Evaluation of noise levels inside and outside the incubator was performed using current signal analysis libraries and toolboxes, and differences between dBA and dBSPL values for the same acoustic noises were investigated. Noise level results were furthermore classed within previously reported sound levels derived from a literature survey. In addition, sound characteristics were evaluated by means of more than 70 temporal, spectral, and modulatory timbre features. Results: Our results show high noise levels related to various real-life situations within the NICU environment. Differences have been observed between A weighted (dBA) and unweighted (dBSPL) values for the same acoustic stimulus. Sonically, the incubator showed a dampening effect on sounds (less high frequency components, less brightness/sharpness, less roughness, and noisiness). However, a strong tonal booming component was noticeable, caused by the resonance inside the incubator cavity. Measurements and a numerical model identified a resonance of the incubator at 97 Hz and a reinforcement of the sound components in this range of up to 28 dB. Conclusion: Sound characteristics, the strong low-frequency incubator resonance, and levels in dBSPL should be at the forefront of both the development and promotion of incubators when helping to preserve the hearing of premature infants.

Temporal and spatial dynamics in soil acoustics and their relation to soil animal diversity.

The observation and assessment of animal biodiversity using acoustic technology has developed considerably in recent years. Current eco-acoustic research focuses on automatic audio recorder arrays and acoustic indices, which may be used to study the spatial and temporal dynamics of local animal communities in high resolution. While such soundscapes have often been studied above ground, their applicability in soils has rarely been tested. For the first time, we applied acoustic and statistical methods to explore the spatial, diurnal, and seasonal dynamics of the soundscape in soils. We studied the dynamics of acoustic complexity in forest soils in the alpine Pfynwald forest in the Swiss canton of Valais and related them to meteorological and microclimatic data. To increase microclimatic variability, we used a long-term irrigation experiment. We also took soil samples close to the sensors on 6 days in different seasons. Daily and seasonal patterns of acoustic complexity were predicted to be associated with abiotic parameters-that is, meteorological and microclimatic conditions-and mediated by the dynamics of the diversity and activity of the soil fauna. Seasonal patterns in acoustic complexity showed the highest acoustic complexity values in spring and summer, decreasing in fall and winter. Diurnal acoustic complexity values were highest in the afternoon and lowest during the night. The measurement of acoustic diversity at the sampling site was significantly associated with soil communities, with relationships between taxa richness or community composition and acoustic complexity being strongest shortly before taking the soil samples. Our results suggest that the temporal and spatial dynamics of the diversity and community composition of soil organisms can be predicted by the acoustic complexity of soil soundscapes. This opens up the possibility of using soil soundscape analysis as a noninvasive and easy-to-use method for soil biodiversity monitoring programs.

Using rectified linear unit and swish based artificial neural networks to describe noise transfer in a full vehicle context.

Vehicle interior noise is a quality criterion of passenger cars. A considerable amount of resources is used to evaluate and design the acoustic environment with respect to given requirements. The customer's perception in the end-of-line vehicle is the main criterion. Therefore, full vehicle testing is a large part of today's sound comfort development. To increase efficiency, it is desirable to limit the hardware testing to a specific component. A later reassembly of the full vehicle is done virtually using transfer functions. These transfer functions of the substructures can be derived numerically or through measurements. However, full vehicle simulations are still challenging. Hence, transfer functions are typically measured but come with the burden of complex procedures. In this work, the authors propose a machine learning algorithm to reduce the effort for finding suitable transfer models in the automotive context. Artificial neural networks with rectified linear unit and swish activation functions are trained on full vehicle measurements. Multiple operation conditions are used for training. The networks compute spectral system responses and relative sensitivities for the input features. The performance is discussed with respect to the full vehicle validation data. The results indicate an effective procedure to reduce the costs of full-size vehicle measurements.

Acoustic metamaterial capsule for reduction of stage machinery noise.

Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work, an acoustic metamaterial capsule is proposed to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta-atoms, which have a simple structure that facilitates manufacturing. Two different metamaterial capsules are designed, simulated, manufactured, and experimentally validated that utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling one to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsules of simple c-shape structure.

Reduction of vibration-induced signal loss by matching mechanical vibrational states: Application in high b-value diffusion-weighted MRS.

PURPOSE: Diffusion encoding gradients are known to yield vibrations of the typical clinical MR scanner hardware with a frequency of 20 to 30 Hz, which may lead to signal loss in diffusion-weighted MR measurements. This work proposes to mitigate vibration-induced signal loss by introducing a vibration-matching gradient (VMG) to match vibrational states during the 2 diffusion gradient pulses. THEORY AND METHODS: A theoretical description of displacements induced by gradient switching was introduced and modeled by a 2-mass-spring-damper system. An additional preceding VMG mimicking timing and properties of the diffusion encoding gradients was added to a high b-value diffusion-weighted MR spectroscopy sequence. Laser interferometry was employed to measure 3D displacements of a phantom surface. Lipid ADC was assessed in water-fat phantoms and in vivo in the tibial bone marrow of 3 volunteers. RESULTS: The modeling and the laser interferometer measurements revealed that the displacement curves are more similar during the 2 diffusion gradients with the VMG compared to the standard sequence, resulting in less signal loss of the diffusion-weighted signal. Phantom results showed lipid ADC overestimation up to 119% with the standard sequence and an error of 5.5% with the VMG. An 18% to 35% lower coefficient of variation was obtained for in vivo lipid ADC measurement when employing the VMG. CONCLUSION: The application of the VMG reduces the signal loss introduced by hardware vibrations in a high b-value diffusion-weighted MRS sequence in phantoms and in vivo. Reference measurements based on laser interferometry and mechanical modelling confirmed the findings.